From OpenClaw to the $25 Billion Real World Assets Market: How AI Agents Transform Tokenization and DeFi

AI Agents as the New Interface for Real World Assets and Blockchain

In March 2026, Illia Polosukhin of the NEAR protocol said, "The users of blockchain will be AI agents."
This view paints a future where artificial intelligence stands in front of blockchain tasks.
Humans will not handle wallets or send transactions.
AI agents work on blockchain tasks and hide their details.

The open-source AI project OpenClaw updated its system with GPT-5.4.
It now connects a context engine so the system works on its own over time and gains firm security.
These changes shift AI from a side role into a main role that works independently with on-chain assets and smart contracts.

Rapid Growth of Tokenized Real World Assets (RWA)

Data from RWA.xyz show tokenized asset value has grown.
Non-stablecoin assets now top $25 billion, up from $6.4 billion at the year’s start.
Six asset types—from US Treasury bonds to commodities and private credit—each passed $1 billion.
This growth ties tokenized assets with decentralized finance and new institutional players.
At scale, the market may back an "agent economy" where AI handles traditional asset classes on-chain.

How AI Agents Are Rewriting the RWA Lifecycle

Asset Issuance: Real-Time Verification Over Manual Due Diligence

Issuing tokenized assets like real estate usually calls for long checks by people.
Legal work, appraisals, and audits cost time and money.
AI agents now join with IoT devices, on-chain credit data, and third-party APIs to check assets in real time and mint tokens fast.
This process cuts work time from months to minutes and shrinks human steps.

Trade Execution: Strategic and Autonomous Decision-Making

AI agents do more than place simple orders.
They use game theory on various on-chain markets.
They spot price differences, read macro signals, and set hedges or stop-loss orders on their own.
This close work among AI agents builds smart market actions that improve trade efficiency.

Asset Management: Continuous Autonomous Monitoring

Tasks such as collecting rent, paying interest, and checking collateral have long been done by people.
AI agents now run these tasks all day.
They assign cash flows, call for extra funds when needed, or sell assets by smart contract rules.
This round-the-clock work makes records clear and correct.

Governance Participation: Algorithmic Proxy Voting

Low voter turnout has often weakened asset governance.
AI agents now review proposals by checking how each one could shift asset worth.
They simulate outcomes and cast votes for investors.
This shift moves governance from occasional events to steady computer-aided decisions.

Emerging Paradigm: Shift from Human to AI Autonomous Management

The join of $25 billion in tokenized assets with self-driven AI shows a big change.
Instead of people alone, real-world assets on blockchain will soon fall under AI control.
AI agents will trade, run, and check these assets on-chain.
The new model ties asset tokenization, decentralized finance, and clear rules to support quick checks, smart trades, and constant management.
This phase brings more institutional choices and changes how financial assets work in the digital field.

Summary

Merging AI agents with blockchain brings a turning point for tokenized assets and asset control.
OpenClaw’s updated system and the rise in asset value above $25 billion show a new model where AI handles issuance, trade, checks, and voting in decentralized finance.
The move from human control to AI control marks more institutional choices and deep changes in how traditional financial assets are digitized and managed on-chain.

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📝 About This Article  

This article was generated by Hivebox AI in collaboration with nGRND.
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